About Me

I am a researcher at the MIT Computer Science & Artificial Intelligence Laboratory (CSAIL) focused on machine learning. I work with Prof. David Gifford in the Computational Genomics group, developing interpretability methods for understanding deep neural networks and investigating approaches for designing therapeutics using ML.

Previously, I completed my undergrad and Masters at MIT, double majoring in computer science and mathematics. I also minored in economics. I graduated in June 2017 (undergrad) and June 2019 (MEng), advised by Prof. David Gifford.

My main interests broadly span machine learning and information, particularly as applied in computational biology and natural language processing. I am also intrigued by systems security and cryptography.

I have had the pleasure to work at Google Brain, Facebook, Bloomberg LP, KAYAK, and Leiden University.

I am originally from Long Island, New York. In my free time I enjoy sailing, hacking on various projects, and world traveling.


What made you do this? Understanding black-box decisions with sufficient input subsets
Brandon Carter*, Jonas Mueller*, Siddhartha Jain, David Gifford
Artificial Intelligence and Statistics (AISTATS), 2019
[Featured as contributed talk at NeurIPS 2018 Workshop on Interpretability and Robustness] [Slides] [Lecture notes] [Code]

Antibody Complementarity Determining Region Design Using High-Capacity Machine Learning
Ge Liu, Haoyang Zeng, Jonas Mueller, Brandon Carter, Ziheng Wang, Jonas Schilz, Geraldine Horny, Michael Birnbaum, Stefan Ewert, David Gifford
Accepted in Bioinformatics, 2019

Critiquing Protein Family Classification Models Using Sufficient Input Subsets
Brandon Carter, Maxwell Bileschi, Jamie Smith, Theo Sanderson, Drew Bryant, David Belanger, Lucy Colwell
ICML Workshop on Computational Biology, 2019. Accepted in Journal of Computational Biology.
[Featured as spotlight talk at ICML Workshop on Computational Biology] [Slides]

Machine learning optimization of MHC class II presented peptides
Haoyang Zeng, Brandon Carter, Siddhartha Jain, Brooke Huisman, Michael Birnbaum, David Gifford
Machine Learning in Computational Biology (MLCB), 2019
[Featured as spotlight talk at MLCB]

Using Deep Learning to Classify the Protein Universe
Maxwell L Bileschi, David Belanger, Drew H Bryant, Theo Sanderson, Brandon Carter, D Sculley, Mark DePristo, Lucy Colwell
bioRxiv: 626507, 2019

Survey of Fully Verifiable Voting Cryptoschemes
Brandon Carter, Kenneth Leidal, Devin Neal, Zachary Neely
MIT Computer and Network Security (6.857) Final Project, 2016

Safety and Efficacy of Ganciclovir Ophthalmic Gel for Treatment of Adenovirus Keratoconjunctivitis Utilizing Cell Culture and Animal Models
Seth Epstein, Karen Fernandez, Brandon Carter, Salma Abdou, Neha Gadaria, Penny Asbell
Investigative Ophthalmology and Visual Science (IOVS), 2012

Interpreting Black-Box Models Through Sufficient Input Subsets
Brandon Carter
M.Eng Thesis, MIT Dept. of Electrical Engineering and Computer Science, 2019

Full listing in Google Scholar.


Click on any of the projects below to learn more. You can also take a look at some of the contributions I have made on GitHub.

Twitter NLP Twitter NLP Follower Prediction
ICU Patient Predictions ICU Patient Predictions
Academics for the Future of Science Academics for the Future of Science
Ploegh Lab Website Ploegh Lab Website
StudentsThink StudentsThink


My email is bcarter [at] csail [dot] mit [dot] edu. Feel free to also connect with me on LinkedIn.